The invention is pertinent to the creation and exploitation of knowledge from an accumulation of unstructured information storage. It provides grounds for relating bodies of knowledge, stored or implied in warehouses, incorporates the expertise of users and generates maps or metadata describing or representing information or knowledge.
The goal of many applications is to gain useful information from unstructured data.
The general phases of such a process would be:    accumulating information, as relevant as possible;    organizations information in warehouses for later retrieval processing;    data mining, aimed to uncover hidden phenomena and knowledge;    searching and retrieving data, using query and visualization technologies;    mapping areas of interest regarding experts and information; and    delivering and monitoring the flow of ‘on-line’ information for all demands.
Data Mining:
Data mining may be defined as the process of discovery of non-obvious valuable patterns, from a collection of data, or alternatively, the automated extraction of hidden predictive information from databases.
Data mining uses a variety of approaches. Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, generally, the convergence and use of artificial intelligence methods and machine learning techniques. In most standard database operations, nearly all of the results presented to the user are something that they knew existed in the database already. Data mining, on the other hand, extracts information that the user did not know existed.
Data mining is generally used to point out interesting information in an accumulation of information. Once such phenomena are discovered, the next challenge is to allow consumers to make use of the new knowledge and insights discovered.
Organization
Organization of information has to support the following criteria:    have the information ready for future retrieval;    have the information ready for future reorganization; and    allow coherent display and visualization of information and metadata of information.
Retrieval
Retrieval is the process by which searching techniques provide results, which are later used to rank and visualize the requested data. Retrieval is usually done by posing queries to an information domain by a user.
Clustering
Knowledge discovery in databases often requires clustering the data into a number of distinct segments or groups in an effective and efficient manner. Clustering is the technique of grouping records together based on their locality and connectivity within an n-dimensional space. Good clusters show high similarity within a group and low similarity between any two different groups.
Clustering Queries
There are existing solutions that monitor the stream of queries and make use of information contained. These typically concern query enhancement for the purpose of better information retrieval. Natural language processing (NLP) terms such as query expansion, query matching, query understanding and semantic enhancement are often used to describe different methodologies whose net goal is the same: providing better and more relevant retrieval to answer the original query of the user, based on information extracted from the stream of past queries. Prior art query solutions generally rely on simple statistical approaches, such as how often various words or phrases appear together in the same document, and cluster words together into semantic “families.”
Most Prior art data mapping techniques lead to “rule books,” which are created and maintained in order to map experts to knowledge. Due to the nature of such techniques, “rule books” are static and are seldomely updated.
Visualization
Wherever there are processes that convert, process, represent or reduce large amount of information, there is a need to represent that raw or processed information to human users, in order to allow them to understand, monitor or analyze the operation and results of those processes. Many Data Mining applications were extremely efficient in processing information, but are quite poor in visualizing their results to human consumers and controllers in a usable manner. The challenge is even greater when it comes to designing visualization models that allow the relation of the operation of Data Mining and other technologies to the daily activities and needs of the operating organizations.
Mapping
There is a need to map areas of interest of organizations to regarding information and people. Most Prior art data-mapping techniques rely on the creation and maintenance of “rule books,” which are created and maintained in order to map experts vs. knowledge. Due to the nature of such techniques, rulebooks are static and are seldom updated.
Therefore, there is a need for a method that overcomes the limitations of the prior art, and provides dynamic adaptive mapping of the areas of interest to the users within an organization, i.e., information derived from unstructured databases, that reflects changes in the interests of the organization.